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The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
Remote sensing images are relevant materials for observation and thematic mapping by multispectral and multi-textural classification. In this paper, we propose classification of urban data with high spectral and spatial resolution. The approach is based on building Differential Morphological Profile (DMP) and then classifying each pixel using Support Vector Machines (SVM) classifier. The DMP is used...
Based on the recent success of Low-Rank matrix Representation (LRR), we propose a novel classification method for robust face recognition, named LRR-based Classification (LRRC). By the ideal that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representations of a set of test vector samples with respect to a set of training vector...
Classification of malicious code by machine learning gives more flexible and adaptable prediction result than by existing approaches [1]. But the approach just can identify looks-like malicious code instead of real malicious one. In this research, a novel method to reduce the vagueness in the classification by machine learning to consider code sequence.
This paper presents a simple and efficient design of a face recognition system, where feature extraction algorithm is employed based on the principle of spatial cross-correlation. In the feature extraction process, instead of processing the entire image at a time, only a pair of rows or columns of an image is considered which makes the algorithm very efficient and low-cost. Considering the cross-correlations...
This paper presents a weighted support vector machine (WSVM) based on association rules for two-class classification problems. The basic idea of the WSVM is to assign different weights to different data points to minimize impacts of outliers. In this paper, we apply association rules to generate weights to prevent bias to the majority class for imbalanced binary classification problems. Experimental...
The automatic insertion of diacritics in electronic texts is necessary for a number of languages, including French, Romanian, Croatian, Sindhi, Vietnamese, etc. When diacritics are removed from a word and the resulting string of characters is not a word, it is easy to recover the diacritics. However, sometimes the resulting string is also a word, possibly with different grammatical properties or a...
The classification of unstructured P2P multicast video streaming is the premise for playing online linkage and real-time evidence in the process of network monitoring management. Based on the classification method in the preliminary research, an improved classification method is proposed. the method uses an optimal feature vector extraction algorithm to filter the proposed behavior features in the...
We discuss an original approach to multidimensional non-stationary time series classification based on dynamic patterns analysis. The main problem in time series classification is construction of appropriate feature space. The success of classification dramatically depends on the quality of the feature space chosen. To construct this space we develop the method for extraction of dynamic patterns that...
The paper presents how solving regression problems can be posed as finding solutions to multiclass classification tasks. The accuracy (averaged over several benchmarking data sets used in this study) of an approximating (hyper)surface to the data points over a given high-dimensional input space created by a nonlinear multiclass classifier is slightly superior to the solution obtained by regression...
During past decades, land use and land cover change detection techniques have undergone substantial development. However, different scenarios and an integrated workflow linking remote sensing imagery and GIS are often neglected. As a result, we develop a land use and land cover change detection and extraction system and propose five scenarios considering data availability and different classification...
The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated...
According to the symmetric characteristics of bispectrum, a novel feature extraction scheme, which includes the summation-at-every-column feature vector, the summation-at-every-row feature vector and their combination in a triangle area, one of the 12 symmetric areas of bispectrum, is proposed. By using One-against-One (OAO) method of multi classification of Support Vector Machine (SVM), the mean...
In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness...
Anti-malware software producers are continually challenged to identify and counter new malware as it is released into the wild. A dramatic increase in malware production in recent years has rendered the conventional method of manually determining a signature for each new malware sample untenable. This paper presents a scalable, automated approach for detecting and classifying malware by using pattern...
The Scale Invariant Feature Transform (SIFT) has been widely used in remote sensing image registration. However, there are many false matches when it is adopted to align images of different sensors. In this paper, a new method based on the combination of image classification and SIFT descriptor is proposed to improve the performance of registration. Experiment results support the potential of the...
Support Vector Machines (SVM) for image classification proved to perform well in many applications. However, they are often not preferred in hyperspectral image analysis due to long processing times caused by a high number of support vectors and large data sets. We present two approaches that speed-up the classification process with SVM by a) simplifying the original SVM, i.e. reducing the number...
Aiming to noise samples in the training dataset, a new method for reducing the amount of training dataset is proposed in the paper which is applicable to text classification. This method describes the distribution of training dataset according to the representativeness score of samples in the class they belong to, so as to show representative samples and noise samples in each class. The new method...
Since seizures in general occur infrequently and unpredictably, it's automatic detection during long term electro encephalograph (EEG) recordings is highly recommended. This paper presents a method of analysis of EEG signals, which is based on time domain analysis. Signal from each channel was divided into different frames of a predetermined length and higher order statistical features were calculated...
Mining is processing data to obtain interesting pattern or knowledge. Noisy EEG can be received on some abnormal state of brain activities. These signals can be logged in data sheets and the samples are taken to identify the rare events. The sampling technique here we used is SMOTE (Synthetic Minority Over-sampling Technique). An approach to the construction of classifiers from imbalanced datasets...
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